I want to read an excel file without setting a header in polars. When i use df = pl.read_excel(file_path, sheet_name = 0) i receive a dataframe with selected column names. But I won't use the first row as header. I need to drop the first 3 rows and then set the top row as the header of the dataframe. How can I do this?
This will help to ignore the header of the excel file.
df = pl.read_excel(filepath, sheet_name=0, read_csv_options={"has_header": False})
Related
I am using azure sql as source dataset and delimited file as sink dataset in the copy activity.
I tried copy activity but First row as header gives comma separated headers.
Is there way to change the header output style ?
Please note spacing is unequal (h3...h4)
In this repro, I tried to give
1 space between 1st and 2nd column,
2 spaces between 2nd and 3rd column,
3 spaces between 3rd and 4th column.
Also, I tried to give same column name for column2 and column3. The approach is as follows.
Data is copied from Azure SQL database to datalake in comma delimitted format as a staging file.
This staging file is taken as a source in Dataflow activity.
In source dataset, first row as header is not checked.
Data preview of Source transformation:
Derived column transformation is added to change the column name of column2 and column3.
In this case, date_col of column1 is header data. Thus when column1 is 'date_col' replace column2 and column3 data with same column name.
column_2 = iif(Column_1=='date_col','ECIX',Column_2);
column_3 = iif(Column_1=='date_col','ECIX',Column_3);
Again derived column transformation is added to concat all the columns with spaces. Column name is given as concat . Value for this column is
concat(Column_1,' ',Column_2,' ',Column_3,' ',Column_4)
Select transformation is added and only concat column is selected here.
In sink, new delimited file is added as a sink dataset. And in sink dataset also , first row as header is not checked.
Output file screenshot
After pipeline is run, the target file looks like this.
Keeping the source as azure sql itself in the dataflow, I created a single derived column 'OUTDC' and added all the columns from the source like this:
(h1)+' '+(h2)+' '+(h3)
Then fed the OUTDC to a delimited sink and kept the Headers option as single string like this:
['h1 h2 h2']
Anyone know how to add header to csv sink? I have a data flow that's source is a database table. Then I have used derived column and concatenated the columns to make one column and split the data in the column by commas (done in the source via a query). I have then selected the column that has been concatenated to be export to csv.
Data example:
Matt,Smith,10
Therefore I technically only have one column, however, I want to add a header for each section of the data.
Desired output:
FirstName,LastName,Age
Matt,Smith,10
You can add headers in CSV file.
Select Data Flow Activity.
Select Source and use Select activity.
Add column names as shown in below screenshot.
Finally add Sink and run Pipeline.
i have as solution which goes like
df1 -->dataframe 1 with having 50 columns of data
df2 --->datarame 2 having footer/trailer 3 columns of data like Trailer,count of rows,date
so i added the remaining 47 columns as "","",""..... so on
so that i can union 2 dataframe:
df3=df1.union(df2)
now if i want to save
df3.coalesce(1).write.format("com.databricks.spark.csv")\
.option("header","true").mode("overwrite")\
.save(output_blob_path);
so now i am getting the footer as well
like this Trailer,400,20210805,"","","","","","","".. and so on
if any one can suggest how to remove ,"","","",.. these double quotes from the last row
where i want to save this file in blob container.
it would be very helpful
You can try to define structure of data frame to treat entire row as single column for both the files and then perform union. This way you no need to add extra columns on data frame 2 and then struck in to tricky situation to remove extra columns after union.
I would like to create a spark dataframe in pyspark from a text file, that has different number of rows and columns and map it to key/value pair, the key is the first 4 characters from the first column of the text file. I want to do that in order to remove the redundant rows and to be able group them later by the key value. I know how to do that on pandas but still confused where to start doing that in pyspark.
My input is a text file that has the following:
1234567,micheal,male,usa
891011,sara,femal,germany
I want to be able to group every row by the first six characters in the first column
Create a new column that contains only the first six characters of the first column, and then group by that:
from pyspark.sql.functions import col
df2 = df.withColumn("key", col("first_col")[:6])
df2.groupBy("key").agg(...)
I have an excel file having 4 worksheets. Each worksheet has first 3 rows as blank, i.e. the data starts from row number 4 and that continues for thousands of rows further.
Note: As per the requirement I am not supposed to delete the blank rows.
My goals are below
1) read the excel file in spark 2.1
2) ignore the first 3 rows, and read the data from 4th row to row number 50. The file has more than 2000 rows.
3) convert all the worksheets from the excel to separate CSV, and load them to existing HIVE tables.
Note: I have the flexibility of writing separate code for each worksheet.
How can I achieve this?
I can create a Df to read a single file and load it to HIVE. But I guess my requirement would need more than that.
You could for instance use the HadoopOffice library (https://github.com/ZuInnoTe/hadoopoffice/wiki).
There you have the following options:
1) use Hive directly to read the Excel files and to CTAS to a table in CSV format
You would need to deploy the HadoopOffice Excel Serde
https://github.com/ZuInnoTe/hadoopoffice/wiki/Hive-Serde
then you need to create the table (see documentation for all the option, the example reads from sheet1 and skips the first 3 lines)
create external table ExcelTable(<INSERTHEREYOURCOLUMNSPECIFICATION>) ROW FORMAT SERDE 'org.zuinnote.hadoop.excel.hive.serde.ExcelSerde' STORED AS INPUTFORMAT 'org.zuinnote.hadoop.office.format.mapred.ExcelFileInputFormat' OUTPUTFORMAT 'org.zuinnote.hadoop.excel.hive.outputformat.HiveExcelRowFileOutputFormat' LOCATION '/user/office/files' TBLPROPERTIES("hadoopoffice.read.simple.decimalFormat"="US","hadoopoffice.read.sheet.skiplines.num"="3", "hadoopoffice.read.sheet.skiplines.allsheets"="true", "hadoopoffice.read.sheets"="Sheet1","hadoopoffice.read.locale.bcp47"="US","hadoopoffice.write.locale.bcp47"="US");
Then do CTAS into a CSV format table:
create table CSVTable ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' AS Select * from ExcelTable;
2) use Spark
Depending on the Spark version you have different options:
for Spark 1.x you can use the HadoopOffice fileformat and for Spark 2.x the Spark2 DataSource (the latter would also include support for Python). See howtos here